1. Statistical Analysis for the 3D Grid
1.1. Calculate hourly/daily percentage change
# calculate hourly/daily percentage change
overall_summary <- overall_summary %>%
mutate(hourly_pct_change = (num_of_ISSR/lag(num_of_ISSR) - 1) * 100,
daily_pct_change = (num_of_ISSR/lag(num_of_ISSR, 24) - 1) * 100) %>%
relocate(hourly_pct_change, .before = ceiling) %>%
relocate(daily_pct_change, .before = ceiling)
overall_summary
1.2. Trend of volume_of_ISSR
task2 <- overall_summary %>%
mutate(month = floor(date/100)%%100)
task2$month<-factor(task2$month)
ggplot(data = task2, mapping = aes(x = datetime_id, y = volume_of_ISSR, fill = month)) +
geom_bar(stat = "identity") +
theme( axis.text.x=element_blank(), axis.ticks.x=element_blank()) +
ylab("volume_of_ISSR (%)") + xlab("Hourly Datetime") +
ggtitle("Trend of hourly updated volume_of_ISSR")
task3 <- overall_summary %>%
group_by(date_id, date) %>%
summarise(avg_volume_of_ISSR = sum(volume_of_ISSR)/24) %>%
ungroup() %>%
mutate(pct_change = (avg_volume_of_ISSR/lag(avg_volume_of_ISSR) - 1) * 100) %>%
mutate(month = floor(date/100)%%100)
## `summarise()` regrouping output by 'date_id' (override with `.groups` argument)
task3$date<-factor(task3$date)
task3$month<-factor(task3$month)
task3
ggplot(data = task3, mapping = aes(x = date, y = avg_volume_of_ISSR, fill = month)) +
geom_bar(stat = "identity") +
theme( axis.text.x=element_blank(), axis.ticks.x=element_blank()) +
ylab("avg_volume_of_ISSR (%)") + xlab("Dates") +
ggtitle("Trend of daily average volume_of_ISSR")
month_days_df = data.frame(month = c(5,6,7,8,9,10,11,12),
month_days = c(15,30,31,31,30,31,30,31))
task5 <- overall_summary %>%
mutate(month = floor(date/100)%%100) %>%
left_join(month_days_df, by = "month") %>%
group_by(month) %>%
summarise(avg_volume_of_ISSR = sum(volume_of_ISSR)/24/min(month_days))
## `summarise()` ungrouping output (override with `.groups` argument)
task5$month<-factor(task5$month)
task5
ggplot(data = task5, mapping = aes(x = month, y = avg_volume_of_ISSR, fill= month)) +
geom_bar(stat = "identity") +
ylab("avg_volume_of_ISSR (%)") + xlab("Months") +
ggtitle("Monthly average volume_of_ISSR") +
geom_text(aes(label = round(avg_volume_of_ISSR, 4)),
vjust = -1.1, size = 3,
show.legend = FALSE)
1.3. Midnight and Noon
midnight_noon <- overall_summary %>%
select(datetime_id, date_id, date, hour, volume_of_ISSR) %>%
mutate(month = floor(date/100)%%100) %>%
filter(hour == 0 | hour == 12)
midnight_noon
midnight_noon$hour <- factor(midnight_noon$hour)
ggplot(midnight_noon, aes(x = date, y = volume_of_ISSR, col=hour)) +
geom_line() +
facet_wrap(~ month, nrow = 2,ncol = 4, scales = "free_x") +
ylab("volume_of_ISSR (%)") + xlab("Dates") +
ggtitle("Volume_of_ISSR at midnight and noon by month") +
theme(axis.text.x = element_text(angle = 30))
month_days_df = data.frame(month = c(5,6,7,8,9,10,11,12),
month_days = c(15,30,31,31,30,31,30,31))
midnight_noon <- overall_summary %>%
select(datetime_id, date_id, date, hour, volume_of_ISSR) %>%
mutate(month = floor(date/100)%%100) %>%
left_join(month_days_df, by = "month") %>%
filter(hour == 0 | hour == 12) %>%
group_by(month, hour) %>%
summarise(avg_volume_of_ISSR = sum(volume_of_ISSR)/month_days)
## `summarise()` regrouping output by 'month', 'hour' (override with `.groups` argument)
midnight_noon
midnight_noon$hour <- factor(midnight_noon$hour)
ggplot(midnight_noon, aes(x = month, y = avg_volume_of_ISSR, col=hour)) +
geom_line() +
geom_point() +
ylab("Monthly average volume_of_ISSR (%)") + xlab("Months") +
ggtitle("Monthly average volume_of_ISSR at midnight and noon") +
geom_text(aes(label = round(avg_volume_of_ISSR, 4)),
vjust = -1.1, size = 3,
show.legend = FALSE)
month_days_df = data.frame(month = c(5,6,7,8,9,10,11,12),
month_days = c(15,30,31,31,30,31,30,31))
midnight_noon <- overall_summary %>%
select(datetime_id, date_id, date, hour, volume_of_ISSR) %>%
mutate(month = floor(date/100)%%100) %>%
left_join(month_days_df, by = "month") %>%
group_by(month, hour) %>%
summarise(avg_volume_of_ISSR = sum(volume_of_ISSR)/month_days)
## `summarise()` regrouping output by 'month', 'hour' (override with `.groups` argument)
midnight_noon
midnight_noon$hour <- factor(midnight_noon$hour)
ggplot(midnight_noon, aes(x = month, y = avg_volume_of_ISSR, col=hour)) +
geom_line() +
ylab("Monthly average volume_of_ISSR (%)") + xlab("Months") +
ggtitle("Monthly average volume_of_ISSR at each hour")
1.4. Ceilings and floors by months
ceiling_floor <- overall_summary %>%
select(datetime_id, date_id, date, hour, ceiling, floor) %>%
pivot_longer(cols = c(ceiling, floor),
names_to = 'type',
values_to = 'pressure_level')
ceiling_floor
ggplot(ceiling_floor, aes(x = datetime_id, y = pressure_level, col=type)) +
geom_line() +
ylab("Pressure Levels (Pa)") + xlab("Hourly Datetimes") +
ggtitle("Ceilings and Floors at each datetime")+
scale_y_reverse()
ceiling_floor2 <- ceiling_floor %>%
group_by(date, type) %>%
summarise(avg_pressure_level = sum(pressure_level)/24) %>%
ungroup() %>%
mutate(month = floor(date/100)%%100)
## `summarise()` regrouping output by 'date' (override with `.groups` argument)
ceiling_floor2
ggplot(ceiling_floor2, aes(x = date, y = avg_pressure_level, col=type)) +
geom_line() +
facet_wrap(~ month, nrow = 2,ncol = 4, scales = "free_x") +
ylab("Daily Average Pressure Levels (Pa)") + xlab("Dates") +
ggtitle("Daily average ceilings and floors by month") +
scale_y_reverse()+
theme(axis.text.x = element_text(angle = 30))
month_days_df = data.frame(month = c(5,6,7,8,9,10,11,12),
month_days = c(15,30,31,31,30,31,30,31))
ceiling_floor3 <- ceiling_floor %>%
mutate(month = floor(date/100)%%100) %>%
left_join(month_days_df, by = "month") %>%
group_by(month, type) %>%
summarise(avg_pressure_level = sum(pressure_level)/24/min(month_days))
## `summarise()` regrouping output by 'month' (override with `.groups` argument)
ceiling_floor3
ggplot(ceiling_floor3, aes(x = month, y = avg_pressure_level, col=type)) +
geom_line() +
geom_point() +
ylab("Monthly Average Pressure Levels (Pa)") + xlab("Month") +
ggtitle("Monthly average ceilings and floors") +
scale_y_reverse() +
geom_text(aes(label = round(avg_pressure_level, 0)),
vjust = 1.5, hjust = "inward", size = 3,
show.legend = FALSE)
2. Statistical Analysis for Layers/Horizontal Grids
layer_summary
2.1. Volume of ISSRs (%) at different pressure lavels by months
month_days_df = data.frame(month = c(5,6,7,8,9,10,11,12),
month_days = c(15,30,31,31,30,31,30,31))
task4 <- layer_summary %>%
mutate(month = floor(date/100)%%100) %>%
left_join(month_days_df, by = "month") %>%
group_by(month, pressure_level) %>%
summarise(avg_volume_of_ISSR = sum(volume_of_ISSR)/min(month_days)/24 ) %>%
arrange(pressure_level)
## `summarise()` regrouping output by 'month' (override with `.groups` argument)
task4$pressure_level <- factor(task4$pressure_level)
task4$month <- factor(task4$month)
task4
ggplot(data = task4, mapping = aes(x = pressure_level, y =avg_volume_of_ISSR, fill = month)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Volume of ISSRs in different months by pressure levels",
x = "Pressure Levels (Pa)",
y = "Volume of ISSRs (%)") +
theme(axis.text.x = element_text(angle = 45, face="bold"),
axis.text.y = element_text(face="bold")) +
theme(plot.title = element_text(size = 15, face="bold.italic"),
axis.title.x = element_text(face="bold"),
axis.title.y = element_text(face="bold"))
ggplot(data = task4, mapping = aes(x = month, y =avg_volume_of_ISSR, fill = pressure_level)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Volume of ISSRs in different months by pressure levels",
x = "Pressure Levels (Pa)",
y = "Volume of ISSRs (%)") +
theme(axis.text.x = element_text(face="bold"),
axis.text.y = element_text(face="bold")) +
theme(plot.title = element_text(size = 15, face="bold.italic"),
axis.title.x = element_text(face="bold"),
axis.title.y = element_text(face="bold"))
3. Latitude Analysis
library(tidyverse)
# setwd("/Volumes/Long_long/DAEN690Dragon/YES_CSV")
latitude_summary <- read.csv("latitude_summary.csv")
latitude_summary
latitude_year <- latitude_summary %>%
group_by(lat_id) %>%
summarise(avg_volume_ISSR = sum(count_cells)/(37*150)/24/229*100)
## `summarise()` ungrouping output (override with `.groups` argument)
latitude_year
ggplot(latitude_year, aes(x = lat_id, y = avg_volume_ISSR)) +
geom_bar(stat = "identity") +
ylab("avg_volume_ISSR (%)") + xlab("Latitude ID (from 1 to 120)") +
ggtitle("Yearly avg_volume_ISSR at each latitude")
month_days_df = data.frame(month = c(5,6,7,8,9,10,11,12),
month_days = c(15,30,31,31,30,31,30,31))
latitude_month <- latitude_summary %>%
mutate(month = floor(date/100)%%100) %>%
left_join(month_days_df, by = "month") %>%
group_by(month,lat_id) %>%
summarise(avg_volume_ISSR = sum(count_cells)/(37*150)/24/min(month_days)*100)
## `summarise()` regrouping output by 'month' (override with `.groups` argument)
latitude_month
latitude_month$month <- factor(latitude_month$month)
ggplot(latitude_month, aes(x = lat_id, y = avg_volume_ISSR)) +
geom_bar(stat = "identity") +
facet_wrap(~ month, nrow = 2,ncol = 4) +
ylab("avg_volume_ISSR (%)") + xlab("Latitude ID (from 1 to 120)") +
ggtitle("Monthly avg_volume_ISSR at each latitude")
4. Longitude Analysis
library(tidyverse)
# setwd("/Volumes/Long_long/DAEN690Dragon/YES_CSV")
longitude_summary <- read.csv("longitude_summary.csv")
longitude_summary
longitude_year <- longitude_summary %>%
group_by(lon_id) %>%
summarise(avg_volume_ISSR = sum(count_cells)/(37*112)/24/229*100)
## `summarise()` ungrouping output (override with `.groups` argument)
longitude_year
ggplot(longitude_year, aes(x = lon_id, y = avg_volume_ISSR)) +
geom_bar(stat = "identity") +
ylab("avg_volume_ISSR (%)") + xlab("Longitude ID (from 1 to 150)") +
ggtitle("Yearly avg_volume_ISSR at each longitude")
month_days_df = data.frame(month = c(5,6,7,8,9,10,11,12),
month_days = c(15,30,31,31,30,31,30,31))
longitude_month <- longitude_summary %>%
mutate(month = floor(date/100)%%100) %>%
left_join(month_days_df, by = "month") %>%
group_by(month,lon_id) %>%
summarise(avg_volume_ISSR = sum(count_cells)/(37*120)/24/min(month_days)*100)
## `summarise()` regrouping output by 'month' (override with `.groups` argument)
longitude_month
longitude_month$month <- factor(longitude_month$month)
ggplot(longitude_month, aes(x = lon_id, y = avg_volume_ISSR)) +
geom_bar(stat = "identity") +
facet_wrap(~ month, nrow = 2,ncol = 4) +
ylab("avg_volume_ISSR (%)") + xlab("Longitude ID (from 1 to 150)") +
ggtitle("Monthly avg_volume_ISSR at each longitude")
